# High-Dimensional Example: q2-classo on 300 Atacama ASVs

This chapter applies q2-classo to the same **300-ASV, 54-sample** Atacama design
used in the [high-dimensional graphical-lasso chapter](../02_graphical_models/06_highdim_atacama.md),
predicting each continuous environmental covariate from the microbiome with
cross-validated log-contrast regression. It reproduces the reference analysis of
Christian L. Müller.

## Setup

Each of the 15 continuous covariates is used **once as the outcome**; missing
outcome values are mean-imputed. Models use the standard log-contrast
formulation **R1** with an intercept, selected by **5-fold cross-validation**
with the **one-standard-error** rule along a log-spaced $\lambda$ path:

```bash
qiime classo regress \
    --i-features atacama-top-300-clr-design.qza \
    --m-y-file atacama-classo-outcomes-mean-imputed.tsv \
    --m-y-column <outcome> \
    --p-do-yshift --p-path --p-path-nlam-log 100 --p-path-lamin-log 0.001 \
    --p-cv --p-cv-subsets 5 --p-cv-seed 1 --p-cv-one-se \
    --p-cv--nlam 100 --p-cv-lamin 0.001 --p-cv-logscale \
    --p-no-stabsel --p-no-lamfixed --p-no-concomitant --p-no-huber --p-intercept \
    --o-result <outcome>-r1-cv5.qza
```

## Prediction from ASVs only (base R1)

The out-of-sample $R^2$ (mean across the 5 folds) shows which environmental
variables are predictable from the 300 ASVs alone:

![q2-classo R1 selected taxa across Atacama outcomes](../../images/png/atacama-full/atacama-top-300-r1-cv5-selected-taxa-heatmap.png)

| Outcome | CV $R^2$ |
|---------|----------|
| extract-concen | 0.61 |
| percent-relative-humidity-soil-100 | 0.48 |
| amplicon-concentration | 0.35 |
| average-soil-relative-humidity | 0.30 |
| elevation | 0.26 |
| relative-humidity-soil-low | 0.26 |
| percentcover | 0.26 |
| temperature-soil-high | 0.24 |
| average-soil-temperature | 0.20 |
| ph | 0.17 |
| temperature-soil-low | 0.13 |
| toc / relative-humidity-soil-high / ec / depth | $\le 0$ |

The first selected taxon (largest-magnitude coefficient column, after the
intercept) is a *Pseudarthrobacter* ASV — a genus characteristic of the Atacama
soil community (see the [interpretation notes](../02_graphical_models/05_interpretation.md)).

## Adjusting for environmental covariates (joint and filtered)

Two variants add the *other* covariates as extra predictors (each rescaled and
L2-normalized, covariate penalty weight `0.1626`):

- **Joint** — add **all** other covariates as predictors.
- **Filtered adjustment** — add only covariates that are **not strongly
  correlated** with the outcome (Pearson $\lvert r\rvert < 0.80$), so a covariate that is
  essentially a proxy for the outcome cannot leak it.

This is a two-step workflow. **First**, build the augmented design by adding the
chosen covariate(s) to the ASV design (this produces new features, constraint,
and weights artifacts):

```bash
qiime classo add-covariates \
    --i-features atacama-top-300-clr-design.qza \
    --m-covariates-file atacama-classo-outcomes-mean-imputed.tsv \
    --p-to-add <covariate> --p-rescale --p-w-to-add 0.162565105 \
    --o-new-features <design>.qza --o-new-c <c>.qza --o-new-w <w>.qza
```

**Then** run the same cross-validated regression as before, but on the augmented
design (passing the constraint and weights from the previous step):

```bash
qiime classo regress \
    --i-features <design>.qza --i-c <c>.qza --i-weights <w>.qza \
    --m-y-file atacama-classo-outcomes-mean-imputed.tsv --m-y-column <outcome> \
    --p-do-yshift --p-path --p-path-nlam-log 120 --p-path-lamin-log 0.0001 \
    --p-cv --p-cv-subsets 5 --p-cv-seed 1 --p-cv-one-se \
    --p-cv--nlam 120 --p-cv-lamin 0.0001 --p-cv-logscale \
    --p-no-stabsel --p-no-lamfixed --p-no-concomitant --p-no-huber --p-intercept \
    --o-result <outcome>-joint-r1-cv5.qza
```

Adding covariates sharply increases predictability of the physically-coupled
variables — e.g. soil temperature and humidity — but the **filtered** analysis
removes most of that gain, showing it came largely from outcome-correlated
covariates rather than the microbiome:

![Filtered-adjustment q2-classo R1 models: microbial and environmental predictors](../../images/png/atacama-full/atacama-filtered-r1-cv5-selected-predictors-heatmap.png)

| Outcome | ASV-only | Joint (all cov.) | Filtered ($\lvert r\rvert<0.8$) |
|---------|----------|------------------|----------------------|
| average-soil-temperature | 0.20 | 0.98 | 0.54 |
| temperature-soil-low | 0.13 | 0.96 | 0.51 |
| temperature-soil-high | 0.24 | 0.94 | 0.56 |
| average-soil-relative-humidity | 0.30 | 0.88 | 0.74 |
| relative-humidity-soil-low | 0.26 | 0.88 | 0.59 |
| elevation | 0.26 | 0.87 | 0.84 |
| percentcover | 0.26 | 0.81 | 0.45 |
| percent-relative-humidity-soil-100 | 0.48 | 0.80 | 0.58 |
| extract-concen | 0.61 | 0.59 | 0.59 |

Elevation stands out: its filtered $R^2$ (0.84) stays close to the joint value
(0.87), i.e. its predictability does **not** depend on outcome-correlated
covariates. For the temperature variables, by contrast, the joint gain
(≈ 0.94–0.98) collapses under filtering (≈ 0.51–0.56), a textbook illustration
of why task-specific covariate filtering matters.